Dialogue State Tracking (DST) is designed to monitor the evolving dialogue
state in the conversations and plays a pivotal role in developing task-oriented
dialogue systems. However, obtaining the annotated data for the DST task is
usually a costly endeavor. In this paper, we focus on employing LLMs to
generate dialogue data to reduce dialogue collection and annotation costs.
Specifically, GPT-4 is used to simulate the user and agent interaction,
generating thousands of dialogues annotated with DST labels. Then a two-stage
fine-tuning on LLaMA 2 is performed on the generated data and the real data for
the DST prediction. Experimental results on two public DST benchmarks show that
with the generated dialogue data, our model performs better than the baseline
trained solely on real data. In addition, our approach is also capable of
adapting to the dynamic demands in real-world scenarios, generating dialogues
in new domains swiftly. After replacing dialogue segments in any domain with
the corresponding generated ones, the model achieves comparable performance to
the model trained on real data.

利用 GPT-4 生成对话数据，通过在 LLaMA 2 上进行两阶段的微调，减少对话收集和注释成本，并表现出比仅使用真实数据训练的基准模型更好的性能，同时适应实际场景中的动态需求。